title: Inference for the effect of treatment on survival probability in randomized trials with noncompliance and administrative censoring.
abstract: In many clinical studies with a survival outcome, administrative censoring occurs when follow-up ends at a prespecified date and many subjects are still alive. An additional complication in some trials is that there is noncompliance with the assigned treatment. For this setting, we study the estimation of the causal effect of treatment on survival probability up to a given time point among those subjects who would comply with the assignment to both treatment and control. We first discuss the standard instrumental variable (IV) method for survival outcomes and parametric maximum likelihood methods, and then develop an efficient plug-in nonparametric empirical maximum likelihood estimation (PNEMLE) approach. The PNEMLE method does not make any assumptions on outcome distributions, and makes use of the mixture structure in the data to gain efficiency over the standard IV method. Theoretical results of the PNEMLE are derived and the method is illustrated by an analysis of data from a breast cancer screening trial. From our limited mortality analysis with administrative censoring times 10 years into the follow-up, we find a significant benefit of screening is present after 4 years (at the 5% level) and this persists at 10 years follow-up.
authors.affiliation: Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA. niehui@wharton.upenn.edu
authors.forename: Hui
authors.initials: H
authors.lastname: Nie
authors.forename: Jing
authors.initials: J
authors.lastname: Cheng
authors.forename: Dylan S
authors.initials: DS
authors.lastname: Small
mesh_terms: D015982:Bias; D001943:Breast Neoplasms / Q000401:mortality* / Q000628:therapy*; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D017063:Outcome Assessment, Health Care / Q000379:methods*; D010349:Patient Compliance / Q000706:statistics & numerical data*; D015995:Prevalence; D011379:Prognosis; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D016019:Survival Analysis; D015996:Survival Rate; D016896:Treatment Outcome